International Business Research; Vol. 11, No. 11; 2018

ISSN 1913-9004 E-ISSN 1913-9012

Published by Canadian Center of Science and Education

187

The Science and Technology Parks (STPs) Evaluation Model

Approach to Eco-Innovation Key Indicator

Mahdi Yami1, Gao Changchun2, Gao Han3

1,2 School of Business and Management, Donghua University, Shanghai, China

3 School of Government, Peking University, Beijing, China

Correspondence: Mahdi Yami, No. 1218, 7e Ave, Montreal, QC., H1B 4J6, Canada.

Received: September 21, 2018 Accepted: October 22, 2018 Online Published: October 26, 2018

doi:10.5539/ibr.v11n11p187 URL: https://doi.org/10.5539/ibr.v11n11p187

Abstract

Science and Technology Park (STP) is one of the most important innovation policies to develop the regional

economy. To manage the STPs successfully, a standard evaluation system as a reference is needed. However,

there is no consensus about the definition of successful STPs due to their different goals and regions. Hence, it is

necessary to establish a reference framework to evaluate the success of different STPs and it is essential to assess

their main goals as the competitive advantage by a set of innovation indicators. This study developed a research

model to evaluate the competitiveness of STPs by analyzing the impact of innovation subjective externalities

based on the Global Innovation Index (GII) and approach to the eco-innovation key indicator. This STP

evaluation model is adopted and tested by two different fuzzy analyzing and examines the survey forms and

questionnaires that have been filled by some STP experts and as a case study all the evidence has been gathered

and analyzed from ―Caohejing Hi-Tech Park‖ in Shanghai and the results evaluated the competitive advantage

via innovation policies and performances and the rating rank contents some innovation main dimensions, key

indicators and factors and also the important result as eco-innovation development and diffusion.

Keywords: science and technology park, innovation evaluation, eco-innovation, competitive advantage, fuzzy,

global innovation index, diffusion

1. Introduction

The Science and Technology Park (STP), is a powerful complex to develop the regional innovation to be set up

as a techno-polis.(Wikipedia, n.d.-b) In recent years a good number of researches involved in evaluating the

STPs to find the best way willing to compare different STPs structures. But as a consequence, many factors

extend on a large set of dimensions affecting the STPs and this feature makes STPs particularly difficult to be

properly evaluated or compared. Hence, a standard STP evaluation index is extremely needed to cover the entire

subjects in policies and performances.(Ferrara, Lamperti, & Mavilia, 2016)

The innovation is the main indicator of the STP evaluation to cover and promote many other factors.(Diez-Vial

& Montoro-Sanchez, 2016) Measuring innovation outcome is a powerful dimension for STPs to evaluate the

capabilities.(Zeng, Xie, & Tam, 2010) However, the most important characteristic of the innovation outcome

measurement approach is eco-innovation development.(Specht, Zoll, & Siebert, 2016) Hence, a reference

framework system is deeply necessary to measure the innovation outcome related to eco-innovation development

result.

This research aims to capture a multi-dimensional evaluation model by discussing the nature of STP according to

the innovation measurement. This innovation large scale, based on global innovation index (GII) is obtained to

evaluate the effectiveness both of the innovation policies and innovation performances. This model as a

comprehensive framework measures the innovation input to output growth upon improving the eco-innovation

development as a strong competitive advantage. The feedbacks of an STP in Shanghai - China as the case study

considered the effectiveness of innovation output on STPs competitiveness.

In recent years many pieces of research tried to illustrate the STP evaluation methods, also there are some STP

evaluation handbooks encouraged by governmental commissions (e.g. European Commissions1, OECD2, etc.)

1www.ec.europa.eu

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but following this kind of paper-based methods would take so much time, cost and administrative affairs. Some

other traditional STP evaluation methods just redefine the limited definitions without proposing a practical

model of evaluation. We are trying to illustrate a practical index with the prompt method and also accurate result

in measuring the outcome key dimensions as the key goals. This index is a multi-level dynamic system,

involving various indicators to follow the objectivity systematic principles.(Xu, Wang, & Wang, 2011)

In section 2 (Literature Review), we follow the introduction by literature review to discuss and dominate the

previously published papers and survey the precedent attempts at STPs evaluation. In section 3, as the

methodology and measurement tools, the main dimensions and comprehensive subjects for STP evaluation is

considered. In section 4, the experimental result of the surveys and questionnaires will explain. In section 5, as

the Discussion, is the main part of our research model to analyze the result and compare with other scholars. In

section 6, concludes the research and we finally signed the paper by section 7 as the references.

2. Literature Review

2.1 Background

―Science and Technology Parks‖ (STPs) play important role in creating the innovation to grow the competitive

advantages. STPs are designed to offer a number of shared services, utility, and product resources, such as

incubators and collaboration facilities to industrial and research plants(Kolahi, 2015) to add value, reduce costs,

improve the environment, and achieve innovation sustainable development.(Wu, Wang, Hong, Piperopoulos, &

Zhuo, 2016) The convergence of policies, performances, and facilities play a key role in the STPs for facilitating

the communication, knowledge flow, new businesses creation and internationalization.(IASP, n.d.) The

―International Association of Science Parks‖3 and ―Association of University Research Parks‖4 are two main

associations related to the STPs.

In many scholars, the literature on STPs has the title of ―Regional Innovation System‖ due to regional input to

output characterized by cooperative innovation.(Tsai & Chang, 2016) This cooperation and interaction in STPs is

emphasized because internal innovation also needs to obtain from universities and other regional firms and it is

highly concentrated on competitive regions to the positive impact the competitiveness.(D. Minguillo, Tijssen, &

Thelwall, 2015) The STPs innovation strategies as knowledge and information technology enhance the

competitive advantages.(Yang & Ying, 2015) The potential of competitive advantage of locating in STPs

increases due to outcome benefits and innovation circulation development.(Ruiz-Ortega, Parra-Requena, &

Garcia-Villaverde, 2016) As a result, the innovation outcome in the STPs is a positive impact on competitive

advantage,(D. Minguillo et al., 2015) and the eco-innovation development is a high factor in innovation

outcome.(Kbar & Aly, 2015)

2.2 The main Dimensions that Affect the STPs

It is necessary to establish a multi-criterion evaluation system as a reference framework to compare the

effectiveness of dimensions to the STPs.(Drawoska, 2011) The most prominent scholars show that some

dimensions have been determined such as Policies, labor markets environment benefits, the availability of

network infrastructure, business start-up rate and market, entrepreneurial knowledge and R&D and finally the

innovation(Alvarez, 2014) that are main effective dimensions on the potential of creating competitive advantages

in STPs. These dimensions can improve all other dimensions of STPs and help to increase the total result of

innovation.(Aliahmadi, Sadeghi, Nozari, Jafari-Eskandari, & Najafi, 2015)

But some existing literature reveals considering the arguments lead us to the innovation output and lack of a

clear evaluation of STPs makes difficult to understand their innovative output.(Liberati, Marinucci, & Tanzi,

2015) One of the main important indicators in innovation output is the eco-innovation and regard to common

innovation benefits defined through identified criteria to evaluate.(Stosic, Milutinovic, Zakic, & Zivkovic, 2016)

1) Policies: The innovation policies from regional government initiated the innovation without central or regional

government's support(Yim, Cho, & Kim, 2015) to shift orientation the regional public and social advantages.(Clark,

2014) To strengthen the efforts of policies is by the innovation evaluation system to raise the innovation

motivations.(Liu & Guan, 2016) A good and perfect evaluation model is to cover the policy innovation to mix with

the performances and make a tool to measure the STPs innovation.(Jordan & Huitema, 2014)

2www.oecd.org

3www.iasp.ws

4www.aurp.net

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2) Human Resource: Regarding human capital theory, human resource quality is adapted to regional innovation

strategy.(Li, Qin, Jiang, Zhang, & Gao, 2015) The rapid development of innovation should focus on social

benefits, job creation and human resource management.(Donate & Guadamillas, 2015) The human resource also

is beneficial to strengthening the advantage of regional innovation.(Chang, Tsai, & Henderson, 2012) Human

resource affection measures to examine the STP location decision. The firms enjoy stronger employment growth

in university STPs.(Wright, Liu, Buck, & Filatotchev, 2008)

3) Network: The STPs are the network-based complex enhance the global innovation by network(Rycroft, 2007)

and relationship increases the innovation by infrastructures and network promotion(Vasquez-Urriago, Barge-Gil,

& Rico, 2016a) to diffuse knowledge on a reciprocal basis.(Díez-Vial & Fernández-Olmos, 2015) The

knowledge obtained from universities by network will make innovation carried out by firms located in the STPs

especially that firms located in central positions of the local firm-network like an intermediate brokerage in the

innovation flows.(Diez-Vial & Montoro-Sanchez, 2016) The STPs structured innovation network, promote the

creation of new businesses and internationalization.(Wu et al., 2016)

4) Market: STPs are as a bridge between innovation strategy and market value. The marketing issue, sales growth,

and profitability are more in STP than off-park firms.(Löfsten & Lindelöf, 2002) The product-based firms linked to

STPs know that the business network value is rather than single activities.(Corsaro, Ramos, Henneberg, & Naudé,

2012) STPs are the results of the marketization and commercialization by innovative activities,(Zou & Zhao, 2014)

foreign investment(Zhang & Sonobe, 2011) creation and global markets.(Liberati et al., 2015) In the STPs the

innovation partners acquire new ideas to create a new competitiveness advantage(Chen, Chen, & Lan, 2016) to add

value through product innovation in the market.(Nishitani & Itoh, 2016)

5) Knowledge: STPs promote knowledge activities as Research and development (R&D), Research and

technology (R&T) and researching results commercialization.(Chun, Chung, & Bang, 2015) The knowledge

investment in the STPs increase the positive impact of regional R&D collaboration, patenting and innovation

factors(David Minguillo & Thelwall, 2015). The R&D cooperation between industries and universities are rather

than research institutes. A knowledge base STP is following the goal of knowledge commercialization as the

output.(Jongwanich, Kohpaiboon, & Yang, 2014)

2.3 Innovation Evaluation

For measuring the innovation, the most important step is to measure the regional innovation

cooperation(Avermaete et al., 2003) and efficacy of innovations to identify strengths and weaknesses in

functions(Mahroum & Alsaleh, 2012) It also defines the intended results of the innovation-based strategic

objective as quality，quantity, efficiency and success.(Stosic et al., 2016) During the innovation process, a new

change in regional innovation output to eco-innovation as a new behavior and culture has an important

position.(Tsai & Chang, 2016)

2.3.1 Innovation

Locate in an STP promotes innovation,(Vasquez-Urriago, Barge-Gil, & Rico, 2016b) so in order to path for the

STPs, we should consider the policy and the performance evaluation index system from several aspects such as

innovative resources input, innovative cooperation process, and output.(Jiang, 2015) Firms without innovation

efforts do not benefit from a park location. To achieve a high innovation return from STPs should measure the

output, enhance the culture of innovation diffusion and also increase the intangible benefits of innovation

flow(Vasquez-Urriago et al., 2016b) due mainly to a more diverse flow(Vasquez-Urriago et al., 2016a) and to

guide the formers, investors and policymakers.(Formica, 2009)

2.3.2 Eco-Innovation

The most important result of innovation output is to create social profit value and eco-innovation as the

competitive advantage.(Herrera, 2015) Social benefits are covered by new products, processes, and services to

lead the business and innovation together to create job, stability, and also reduce the overall negative impact on

the environment.(Stosic et al., 2016) Eco-innovation is a social innovative output toward new "ecological" ideas

to contribute the sustainable developments(Wikipedia, n.d.-a) and share the strong knowledge-based economy

and latest technologies. Hence, a proper eco-system plays an important role in creating innovation by identifying

the real value of innovation to achieve the STPs optimal goal achievement.(Kbar & Aly, 2015) Eco-innovation

development should be in direction of eco-innovation diffusion.(Karakaya, Hidalgo, & Nuur, 2014)

Socio-economic outcomes of eco-innovation has main effects of eco-innovation activities for society and the

economy, that includes changes in employment, turnover by export of products from eco-industries, circular

economy and employment. (See Note 1)

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2.3.3 The Global Innovation Index (GII)

One of the challenges in the multi-dimensional innovation assessment is the innovation development(Stosic et al.,

2016) to emphasize competitiveness (Martinsuo & Poskela, 2011) and to cover goals beyond the innovation

output. (Gamal, Salah, & Elrayyes, 2011) Porter in 1990 noted that the STPs are different because the difference

of some policies or performances in the nations(Sadeghi & Sadabadi, 2015) so, not only an index to compare the

STPs in a country, but also we need a new index which is amenable to global comparisons.(Manrique &

Velásquez, 2011)

The Global Innovation Index (GII) is an annual ranking of 141 countries economy to compare their level of

innovation derived from several sources to create a system in which innovation factors are continually evaluated.

(WIPO, 2015) GII provides a multi-criteria tool and a rich database to promote long-term output growth.(GII,

2015) To measure the innovation the majority of methods are assessing just the performances without policies,

but GII is a global ranking to compare countries by their level of success in innovation based on both policies

and performances, input-output indicators and subjective-objective data. GII is suitable to evaluate the STPs in

complex and wide countries (as like as China).

2.4 The STPs in China

From 1990 under the Torch program, China established and developed more than 80 STPs on 53 china‘s

metropolises due to the concentration of high technology Small and Medium Enterprises (SMEs).(Hu, 2007)

Shanghai as the most populous metropolis in China with more than 24 million population (2014) is a global

business hub(Wikipedia, n.d.-c) with more than thirteen technology-industrial zones that two science-technology

parks in this group are the biggest one as ―Zhangjiang Hi-Tech Park‖ and ―Caohejing Hi-Tech Park‖.

We had some interviews by the Zhangjiang Hi-Tech Park experts. This park specializes in research of life

sciences, software, semiconductors, and information technology. In some circles, the park is also known as

China's Silicon Valley.(Wikipedia, n.d.-d) This park with Caohejing Hi-Tech Park focuses on the development of

information industry, new materials, environmental protection, new energy, and other industries, while the

introduction of a high level of supporting services.(caohejing-Park, n.d.) The ―Caohejing Hi-Tech Park in

Shanghai‖ in China based on the literature, have been weighted by outcomes of the innovation by considering

the total dimensions and effectiveness.

3. Methodology and Measurement

In every evaluation methodology, the appropriate choice of measurement will depend upon the scorecard

framework based on the impact of main dimensions and the key indicators.(Perrin, 2002) Regarding the previous

studies, the best method to evaluate the STP is to prepare a multi-criteria innovation index as an evaluation

framework. Our new model is including some innovation metrics, investigating innovation indices, dimensions

different innovation indicators and factors to consider this idea. Our method offers an effective way of research

by administered surveys and interviews (face-to-face) as follows:

3.1 The Initial Interviews (First Step)

At first the three science parks of ―ZhongGuanCun Science Park‖ in Beijing, ―Zhangjiang Hi-Tech Park‖ and

―Caohejing Hi-Tech Park‖ in Shanghai was selected to do some initial interviews refer to related experts who are:

The administrative committee of ―ZhongGuanCun Science Park‖ in Beijing, Business development center of

―Zhangjiang Hi-Tech Park‖ and project supervisor in ―Caohejing Hi-Tech Park‖ in Shanghai.

In all interviews five categories determined as the main influencing dimension to innovation in the STPs:

Policies, Human Resources, Network, Market and knowledge. However, many innovation evaluation indices

(See Note 2) was studied to find the innovation outcome factors to follow the goal of eco-system in the STPs and

Global Innovation Index (GII)5.

The entire dimensions have been prepared based on GII innovation scopes due to a fruitful development

perspective by innovation input and output. However, some weakness of GII includes indicators that go beyond

the traditional measures of innovation (GII, 2015) and also it cannot make a strong relationship among indicators

representing input and output.(Sohn, Kim, & Jeon, 2016) Hence, we have decided to add ―eco-innovation

development‖ in the output as one of the main new STP goals of eco-system and also make a strong chain

between innovation input and output by fuzzy AHP process and DEA process.

5www.globalinnovationindex.org

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3.2 The Surveys (Second Step)

We prepared a questionnaire presented two main groups of studies composed of our case study. The first group

investigates the ranking and determines the seven key dimensions in the STPs according to GII scopes. The

second group insists on new innovation output indicator as eco-innovation development. Upon the feedback also

the question reports and forms finished with asking their views, weakness or advantage of our evaluation index.

The surveys sent to the abovementioned experts of the three parks who had interviewed before. After their

modification proposals, the approved result of the dimensions is shown in Table 2 (in Results), and the key

indicators of STPs organized as shown in Table 3 (in Results), as the criterion index.

Table 1. The evaluative scales

AHP Scale affection (9 points) Likert-Type scale affection (5 points) Parentage scale affection (5 points)

Qualitative

variables

Quantitative

Value

Qualitative

variables

Quantitative

Value

Qualitative

variables

Quantitative

Value

No affect 1 No affect 0 No affect 0<V≦20%

Minor affect 3 Minor affect 0.5 Minor affect 20% <V≦40%

Neutral 5 Neutral 1 Neutral 40%<V≦60%

Moderate affect 7 Moderate affect 1.5 Moderate affect 60%<V≦80%

Major affect 9 Major affect 2 Major affect 80% <V ≦100%

In Table 1, every qualitative variable evaluated by five-level response anchors V = (v1, v2, v3, v4, v5) relative to

affection with five points quantitative value and nine points for AHP scale. After finalizing the scorecard and the

impact, the questionnaires sent to 10 experts in ―Caohejing Hi-Tech Park‖ in order to define the affections. All

the experts selected in the position of the CEO, HR manager, R&D manager and etc.

3.3 The Analysis (Third Step)

After gathering hand-collected data of the questionnaires and also data were collected using semi-structured

interviews, all the non-parametric statistical calculation has been analyzed to identify the weights and

efficiencies of the STPs by MATLAB. Two different fuzzy methods as AHP and DEA are selected to separately

make the framework for analyzing, modeling, and optimization the uncertainties.

The Analytic Hierarchy Process (AHP) is a structured technique for analyzing the best goal and understanding of

the problems of the goals. It provides a rational framework to structure and quantify the elements and also to

relate the elements to overall goals and alternatives. In addition, Data Envelopment Analysis (DEA) is the

method of productive efficiency in operations management where a set of measures is selected relating multiple

outputs to multiple inputs to be estimated. The AHP issued the weights of the efficiency of innovation inputs and

outputs separately and then in another process of DEA we linked the innovation input indicators to the output

goals. Finally, we concluded by comparing the results in two different methods.

4. Results

In ―Caohejing Hi-Tech Park‖ from 10 questionnaires in total refers to the experts, 5 answered survey forms

actual gathered of which three were valid as the statistical society. (The most problem is that all the experts are in

high administrative position and not accessible).

Table 2. The main dimensions

A. INNOVATION INPUT B. INNOVATION OUTPUT

I1 Policies O1 Knowledge & tech outputs I2 Human Resource O2 Creative outputs I3 Network O3 Eco - Innovation Development I4 Market I5 Knowledge

In Table 2, The dimensions according to the replied questionnaires have constructed, and at the first survey, all

the experts confirmed the ―eco-innovation development‖ as a necessary goal. But in interviews, they insisted that

this goal needs some infrastructures as green early stage investments and also environmental and energy R&D

outlays. They mentioned the result will available as Eco-innovation related patents, academic publications and

media coverage in the science section. In the technology section, it will be available as Exports of products from

eco-industries, circular economy.

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Table 3. Criterion, the key Indicators relative to the main dimensions

Ii 1st Level,

Dimensions

Ai 2nd level – Indicators Oi 1st Level,

Dimensions

Bi 2nd level – Indicators

I1 Institutions

(Policies)

A1 Policy O1 Knowledge and

technology

outputs

B1 Knowledge Diffusion

A2 Laws B2 Knowledge Creation

A3 Ease of Business B3 HR knowledge development

I2 Human

Resources

A4 Education O2 Creative outputs

B4 Intangible Assets

A5 High Educated labors B5 Creation

A6 Employment policies B6 Online Creation

I3 Infrastructure A7 ICTs O3 Eco - Innovation

Development

B7 Eco – social process

A8 General Infrastructure B8 Eco – Environment

A9 Ecological Sustainability B9 Eco – technological term

I4 Market A10 Investment

A11 Credit

A12 Trade and Competition

I5 Knowledge A13 Knowledge Linkages

A14 Knowledge performance

A15 Knowledge Absorption

In Table 3, Regarding the GII and studies about the eco-innovation, all 24 indicators set the dimensions A1-A15

for innovation input, and B1-B9 for innovation output. Then a 24×24 matrix has prepared but in the first

consideration, the experts pointed that this questionnaire (matrix) is huge and baffling (For example in O1 the B1,

B2, and B3 are in the same goal group). Hence we ignored the output indicators because our main goal is

considering the main output goals, not output indicators. So we constructed the frameworks as innovation input

and innovation output separately, and analysis structured for inputs by indicators and for outputs by dimensions.

4.1 Analysis by AHP Method

According to the replies in questionnaires, after adjusting the data by AHP scale relative affection 1-9 points, the

pairwise comparison matrix outcomes by equation (1). ―A‖ is the alternatives based on i:

𝑨𝑨𝑯𝑷𝒊 = ∑ 𝑎𝑖𝑗

𝑛

𝑗=1𝑤𝑗 (1)

Table 4. AHP method for innovation outputs

124

2/112

4/12/11

O1 O2 O3

Pairwise

Comparison

Matrix

O1 1.00 0.50 0.33

O2 2.00 1.00 1.00

O3 3.00 1.00 1.00

Sum 6.00 2.50 2.33

Standardized Matrix

O1 O2 O3 Total Average Weight Cons. Average

O1 0.17 0.20 0.14 0.51 0.170 18% 3.01 C = 3.02

O2 0.33 0.40 0.43 1.16 0.388 38% 3.02 CI = 0.01

O3 0.50 0.40 0.43 1.33 0.443 44% 3.03 RI = 0.58

Sum 1.00 1.00 1.00 3.00 1.000 100%

CR = 0.01578326

CR= 0.01 is True (CR <=0.1)

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In Table 4, according to the replies in questionnaires, after adjusting the data by AHP scale relative affection 1-9

points, the alternatives in terms of all criteria by equation (1) resulted in the weight of O1 is 18%, O2 is 38% and

O3 is 44%.

ʎ𝒎𝒂𝒙 = ∑(𝑺.𝑽)

𝒏.𝒘𝒗𝒋

𝒏

𝒋=𝟏

(2)

And:

𝐶𝐼 =ʎ𝑚𝑎𝑥−𝑛

n−1 (3)

The AHP method for innovation output is by CR as:

𝐶𝑅 =CI

RI (4)

In equation (2), ―V‖ is the value of ―i‖th output and ―j‖th input. Regarding to ―CI‖ equation (3), if the final value

of innovation output ―CR‖ is no higher than 0.1, as equation (4) all the process is approved. (Saaty, 1980).

Table 5. AHP method for innovation inputs

11218

1122/17

2/12/112/14

12219

8/17/14/19/11

Pairwise

Comparison

Matrix

I1 I2 I3 I4 I5

I1 1.00 0.11 0.25 0.14 0.13

I2 9.00 1.00 2.00 2.00 1.00

I3 4.00 0.50 1.00 0.50 0.50

I4 7.00 0.50 2.00 1.00 1.00

I5 8.00 1.00 2.00 1.00 1.00

Sum 29.00 3.11 7.25 4.64 3.63

Standardized Matrix

I1 I2 I3 I4 I5 Total Average Weight Consistancy Average

I1 0.03 0.04 0.03 0.03 0.03 0.17 0.034 3% 5.05 C = 5.05

I2 0.31 0.32 0.28 0.43 0.28 1.61 0.323 32% 5.09 CI = 0.01

I3 0.14 0.16 0.14 0.11 0.14 0.68 0.136 14% 5.04 RI = 1.12

I4 0.24 0.16 0.28 0.22 0.28 1.17 0.234 23% 5.04

I5 0.28 0.32 0.28 0.22 0.28 1.36 0.273 28% 5.04

Sum 1.00 1.00 1.00 1.00 1.00 5.00 1.000 100%

CR = 0.01100954

CR= 0.01 is True (CR <=0.1)

In table 5, in inputs separately condition, the same method and is tested by CR value. The data were normalized

using the hybrid method of DEA. The affection weights matrix had set up by the values of questionnaires in

finding the affection value of outputs on inputs.

4.2 Analysis by DEA Method

Data Envelopment Analysis (DEA) method is useful for weight restrictions conditions when judgement values

are in the form of additional constraints on the input and output weights. These constraints reduce the flexibility

of weights and improve the deviation in the DEA model. The optimum weight of the input-oriented multiplier in

DEA method extends the same modification to the output-oriented model in terms of variable input and output

weights (multipliers model). If all weight restrictions are separated the multiplier model correctly identifies the

optimal weights. But, problem arises when at least one weight is linked. In this case the optimal weight is not in

the best comparison. (Podinovski, 2016)

Hence, we should approach to separate the innovation input and output is possible because the first identified and

the smooth surface are fitted.(Sreedevi, Vimala, Rameswari, Sateesh, & Professor, 2016) But we should make a

powerful link by the DEA fuzzy method to find the weight and affection of inputs to output as a strong bridge.

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If ―V‖ is the value of ―i‖th output and ―j‖th input, ―W‖ is the normalized weight and ―O‖ is the optimum weight,

Then:

Final W = ∑ 𝑊𝑖𝑛𝑖=1 ∑ 𝑉𝑖𝑗

𝑛

𝑗=1/𝑂𝑖𝑗/𝑛 (5)

The total weight of inputs and outputs calculated and adjusted by equation (5), the final W represents the

optimum weight as the decision-making unit under the assessment.

Table 6. Ranking result by Optimization

I1 I2 I3 I4 I5 Total

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

O1 0 0.5 0.5 1.5 1 1 0.5 0.5 0 1 1 0.5 1 0.5 0.5 10 0.18

O2 0.5 1 1 2 2 2 1 1 0.5 2 1.5 1 2 2 1.5 21 0.38

O3 1 1 1 2 2 2 1.5 1.5 1.5 2 1.5 1 2 2 1.5 23.5 0.44

Sum 1.5 2.5 2.5 5.5 5.0 5.0 3.0 3.0 2.0 5.0 4.0 2.5 5.0 4.5 3.5 54.5 Total

O.V. 2.8 4.6 4.6 10.1* 9.2 9.2 5.5 5.5 3.7 9.2 7.3 4.6 9.2 8.3 6.4 90 100

In Table 6, The optimum weight = Total weight*100/54.5(%) and A4 in I2 is 10.1 as the optimum indicator. The

weight for O1 is 18%, O2 is 38% and O3 is 44% as well as the weights by AHP method.

Table 7. Ranking result by Normalization

O1 0.00 0.05 0.05 0.15 0.10 0.10 0.05 0.05 0.00 0.10 0.10 0.05 0.10 0.05 0.05 1.00 18

O2 0.02 0.05 0.05 0.10 0.10 0.10 0.05 0.05 0.02 0.10 0.07 0.05 0.10 0.10 0.07 1.00 38

O3 0.04 0.04 0.04 0.09 0.09 0.09 0.06 0.06 0.06 0.09 0.06 0.04 0.09 0.09 0.06 1.00 44

Sam 0.07 0.14 0.14 0.33 0.28 0.28 0.16 0.16 0.09 0.28 0.24 0.14 0.28 0.23 0.19 3.00 100

In Table 7, we estimated the final percentage weights of the output dimensions and also the final normalized

weights of input indicators, to the sum of the criteria as innovation outcomes the total weights of the indicators

equaled one.

5. Discussion

The total results of the correlational analysis show that the weight of affection of the main dimensions in both

fuzzy methods of DEA and AHP are the same and the weight of input indicators should be flexible enough

because some regional differences and relatives regard to regional economic benefits. All the studies hereby as a

knowledge-based discussion have been presented:

Figure 1. AHP method for innovation inputs

Regardless the traditional STPs measuring systems, in MATLAB structured model as Fig 1., the main dimensions

categorized the innovation process by input and output according to the GII scopes with eco-system main goals.

The effective dimension in innovation inputs is the Human resources (I2) and in the outputs is the eco-innovation

development (O3) with the highest impact for competitive advantage. The analysis also results that the Institutions

and Policies (I1) is in the less affection, however some interviewees in Beijing mentioned that in innovation input,

the policy is more important. It shows that how much the weights of the index should be flexible in every

environment. One unanticipated finding that some experts mentioned is the ―eco-innovation diffusion‖ that should

add to the indicator in the further studies (after totally the goal of eco-innovation will achieve).

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Figure 2. All the outputs in one layer Figure 3. The outputs in two layers

Regarding interviews, the initial structure of the regional Innovation System from inside-out to outside-in is

characterized by cooperative innovation as Fig.2(Shukla, Gupta, Dubey, & Thakur, 2016) but with respect to the

experts view, a new approach for the STPs model should be able from inside-out to outside-in with two layers in

output that eco-innovation will be the result of the two another outcome dimensions as Fig.3.

Figure 4. The most important indicator by MATLAB Result

According to Figure 4, the most effective indicator in innovation inputs is ―Education‖ (I4) with the optimum

weight of 10.1, in MATLAB, the conclusion of weights (between max 1 and the min 0) and outcome variables

issued by percentage to decide easier.

Table 8. The factors of indicators

A. Innovation Input

1 Political Effectiveness 2 Law effectiveness 3 Ease of Business # Commercial policies (Customs, Tax exemption, Tariff rate) 4 Education Effectiveness # Investment on education

5 High quality Educated # Researcher, engineer, Manager, Business provider (Marketer), # Experts (Academic or experimental and training)

6 Employment policies # Develop opportunities for university students, # Employment in well paid high sector jobs, # Females Employed with advanced degree

7 ICTs Effectiveness # ICT access, # Government's online service, # Communications, computer and information services

8 General Infrastructure # Electricity output, # Logistics performance 9 Ecological Sustainability # environmental certificates 10 Credit # Ease of getting credit 11 Investment # Ease of investment absorption, # Ease of protecting investors

12 Trade and Competition # local competitiveness, # Domestic market share, # Improving Export power, #Accessibility to the market information and data, # Commercializing, # Market development

13 Knowledge economy # Formal training (especially for the start-up companies), # Gross expenditure on R&D(GERD) , # Knowledge Investment, # Royalties and license fees payments

14 Knowledge Linkages # Government collaboration, # Industry sections collaboration, # High ranking universities cooperation, # State of cluster development, # Relationship with other enterprises, # Joint venture / strategic alliance deals

15 Knowledge Absorption # Accessibility to the technology resources and information, # High-tech imports

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B. innovation outputs

16 Knowledge Creation #Patenting and licensing activities, #Scientific and technical publications, #Total computer software spending, #High-tech and medium-high-tech output

17 Knowledge Impact #Growth rate of GDP per person engaged, #New business density, #Quality certificates

18 Knowledge Diffusion #High-tech exports, #Foreign direct investment net outflows, #R&D, Technology and knowledge transferring

19 Intangible Assets #Trademark and branding, #Business and organizational models creation

20 Creation #Cultural and creative goods exports, #Cultural and creative services exports, #Global entertainment and media output

21 Online Creation #Generic top-level domains (gTLDs), #Dynamic Website monthly edits

22 Eco – Environment

# Innovation economy (Capital, investment and financial supports), # Investment for innovative ideas, # Internationalization (innovation that compete internationally), #Innovation opportunities realized in firms, # Innovation Policy (Small business development through incubators), #Modification the SPT Operational management team

23 Eco – social process # Culture of Innovation (Awareness of innovation beneficial effects), # The social benefits, # Sustainable job creation

24 Eco – technological term # knowledge economic development, # Numbers of (Technological - based)company start-ups

In Table 8, the main factors that the experts concentrated as the different forms of linkage activities and

implications of relevant evidence and the scope for promotional policies. They insist the main benefit of

eco-innovation is the social advantages for the located hi-tech enterprises. In addition, we discovered that

―Caohejing Hi-Tech Park‖ has the transportation problem due to located inside the Shanghai (not in the suburb).

Figure 5. The MATLAB Result of achieving of the goal in Caohejing Hi-Tech Park

The results in Figure 5 shows all pairwise matrix and results present that ―Caohejing Hi-Tech Park‖ has a good

score with a significant impact on innovation development and effective value toward eco-system. Finally all of

our studies and interviews confirmed by experts and interviewees that the main further competitive advantage in

STPs is the ability and capacity of creating innovation by ―eco-innovations diffusion‖. The high-tech clusters in

regional economies are presenting an obstacle of realistic assessments of policy and performances relevance.

6. Conclusion

Concluding, the new goal of the STPs is to develop the innovation process to a proper eco-innovative system by

a novel analytical approach. The hypothesis of this study was undertaken to design a multi-criterion reference

framework to measure the effectiveness of the key indicators to achieve the eco-STP system. According to

relevant surveys and literature, we associated main key dimensions that are assessed by comparison of different

scales and one of the more significant emerges from this study is a new method that has been adopted the

traditional scorecards with GII scopes and has established to add the eco-innovation development as a final key.

Overall, the current study is based on a small sample by analyzing an STP in Shanghai and we tried to provide a

functions-based operational System by fuzzy methods (AHP and DEA) and finally, we are witnessing that the

eco-innovation system is the base of STP competitive advantage evaluation. This study strengthens to separate

the innovation process by input and output and also to make a strong comparison link between them by fuzzy

evaluation of their affection.

In summary, the top-three dimensions of STPs based on the regional innovation outcome are ―Knowledge and

technology outputs‖, ―Creative outputs‖ and ―Eco-Innovation Development‖ and the main input indicators that

are influencing the effectiveness are the ―education‖ in ―Human resources‖ dimension, ―Investment‖ in ―Market‖

dimension and also the ―Knowledge linkage‖ in ―Knowledge‖ dimension.

0

20

40

60

O1 O2 O3

Main Scopes Comparision Chart

系列1 系列3

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The final conclusion as the general overview of the literature associated some interesting results that the main

benefit of an eco-STP system is to ecological ideas and Culture of Innovation, the stable social benefits (job

creation), internationalization, industry cluster effect and finally to share the strong knowledge-based economy

and latest technologies (eco-innovation diffusion). It was also understanding that the eco-innovation as a new

competitive advantage for the located hi-tech enterprises is effective.

Future research should concentrate on the investigation of ―diffusion of eco-innovations‖ that actually

corresponds to the next research paths.

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Notes

Note 1. European Commission (2017). Eco-innovation [online]. Available at:

https://ec.europa.eu/environment/ecoap/indicators/index_en

Note 2. Bloomberg Innovation Index, Bogota Manual Index, Oslo Manual Index, Florida Creative Class Index,

Global Competitiveness Report, Information Technology and Innovation Foundation (ITIF) Index,

Innovation Capacity Index (ICI), Indiana Innovation Index, Innovation Union Scoreboard, Innovations

Indikator, INSEAD Innovation Efficacy Index, International Innovation Index, National Association of

Manufacturers (NAM), Management Innovation Index, New York City Economic Development

Corporation Innovation Index (NYCEDC), State Technology and Science Index, World Competitiveness

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